Model identification of Solid Oxide Fuel Cell using hybrid Elman Neural Network/Quantum Pathfinder algorithm

Jia, Hailong; Taheri, Bahman

doi:10.1016/j.egyr.2021.05.070

Cited by 28 publications

(8 citation statements)

References 41 publications

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“…The hidden layer has m nodes, which are connected to the hidden layer at the previous moment. The weight matrix connecting the hidden layer uses random data to prepare the hidden layer [ 17 , 18 ].…”

Section: Design Of the Methods For Extracting Events From The Knowled...mentioning

confidence: 99%

Event Scene Method of Legal Domain Knowledge Map Based on Neural Network Hybrid Model

Zhou

2022

Applied Bionics and Biomechanics

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Event extraction technology is one of the important researches in the field of information extraction, which helps people accurately retrieve, find, classify, and summarize effective information from a large amount of information streams. This paper uses the neural network hybrid model to identify the trigger words and event categories of the legal domain knowledge graph events, extracts the events of interest from a large amount of free text, and displays them in a structured format. First, the original text is preprocessed, and then, the distributed semantic word vector is combined with the dependent syntactic structure and location attributes to create a semantic representation in the form of a vector. The combined deep learning model is used to extract activated words, the long-term memory loop neural network uses temporal semantics to extract deep features, and the convergent neural network completes the extraction of activated words and event categories. Finally, the experimental results show that the accuracy of event extraction of the neural network hybrid model designed in this paper has reached 77.1%, and the recall rate has reached 76.8%, which is greatly improved compared with the traditional model.

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Section: Design Of the Methods For Extracting Events From The Knowled...mentioning

confidence: 99%

Event Scene Method of Legal Domain Knowledge Map Based on Neural Network Hybrid Model

Zhou

2022

Applied Bionics and Biomechanics

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“…Zhang et al, (2021b) propose a novel optimal model of extreme learning machines (ELM) network based on the improved red fox optimization (CRFO) algorithm, upon which parameter identification under nonlinear dynamic behavior of the SOFC stack can be perceived by comparing with the other two methods. Based on the minimizing mean squared error (MSE) between empirical and modeled data, a new hybrid Elman neural network (ENN) method is designed to track unknown parameters of the SOFC efficiently and accurately, which is combined with the quantum pathfinder (QPF) algorithm, called QPF base ENN (QPF-ENN) (Jia and Taheri, 2021). There is no doubt that the use of various meta-heuristic algorithms to optimize the control parameters of ANN models can take into account the advantages of both, so as to significantly reduce fitting errors and improve accuracy.…”

Section: Methods Of Parameter Identificationmentioning

confidence: 99%

Parameter Identification for Solid Oxide Fuel Cell Models: Crucial Comments

2022

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“…The interaction between the layers are considered as FFCNs (feed-forward connection networks). The context layers of Elm NN are used to contain the hidden layers' output values [30]. Elman NN is, generally, regarded as a feedback NN with tap delay layers.…”

Section: Elman Neural Network (Elm Nn)mentioning

confidence: 99%

Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia

Yassin

Tawabini

Al-Shaibani³

et al. 2022

Molecules

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Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil.

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Model identification of Solid Oxide Fuel Cell using hybrid Elman Neural Network/Quantum Pathfinder algorithm

Cited by 28 publications

References 41 publications

Event Scene Method of Legal Domain Knowledge Map Based on Neural Network Hybrid Model

Event Scene Method of Legal Domain Knowledge Map Based on Neural Network Hybrid Model

Parameter Identification for Solid Oxide Fuel Cell Models: Crucial Comments

Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia

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